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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: facebook/deit-base-patch16-224 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: deit-base-patch16-224-finetuned-stroke-binary |
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results: [] |
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datasets: |
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- BTX24/tekno21-brain-stroke-dataset-binary |
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--- |
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# deit-base-patch16-224-finetuned-stroke-binary |
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This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on an BTX24/tekno21-brain-stroke-dataset-binary dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1527 |
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- Accuracy: 0.9489 |
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- F1: 0.9484 |
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- Precision: 0.9505 |
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- Recall: 0.9489 |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 48 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.1646 | 0.6202 | 100 | 0.1588 | 0.9430 | 0.9425 | 0.9442 | 0.9430 | |
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| 0.1417 | 1.2357 | 200 | 0.1640 | 0.9439 | 0.9433 | 0.9458 | 0.9439 | |
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| 0.1681 | 1.8558 | 300 | 0.1622 | 0.9453 | 0.9447 | 0.9470 | 0.9453 | |
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| 0.1512 | 2.4713 | 400 | 0.1510 | 0.9435 | 0.9430 | 0.9441 | 0.9435 | |
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| 0.1506 | 3.0868 | 500 | 0.1913 | 0.9340 | 0.9327 | 0.9391 | 0.9340 | |
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| 0.1654 | 3.7070 | 600 | 0.1679 | 0.9426 | 0.9419 | 0.9442 | 0.9426 | |
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| 0.1482 | 4.3225 | 700 | 0.1551 | 0.9403 | 0.9402 | 0.9402 | 0.9403 | |
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| 0.1599 | 4.9426 | 800 | 0.1489 | 0.9462 | 0.9457 | 0.9471 | 0.9462 | |
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| 0.1477 | 5.5581 | 900 | 0.1437 | 0.9426 | 0.9424 | 0.9425 | 0.9426 | |
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| 0.1308 | 6.1736 | 1000 | 0.1527 | 0.9417 | 0.9414 | 0.9416 | 0.9417 | |
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| 0.1362 | 6.7938 | 1100 | 0.1608 | 0.9426 | 0.9421 | 0.9432 | 0.9426 | |
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| 0.1494 | 7.4093 | 1200 | 0.1601 | 0.9435 | 0.9429 | 0.9451 | 0.9435 | |
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| 0.1592 | 8.0248 | 1300 | 0.1430 | 0.9430 | 0.9429 | 0.9429 | 0.9430 | |
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| 0.16 | 8.6450 | 1400 | 0.1504 | 0.9457 | 0.9451 | 0.9475 | 0.9457 | |
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| 0.1245 | 9.2605 | 1500 | 0.1506 | 0.9462 | 0.9458 | 0.9470 | 0.9462 | |
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| 0.1397 | 9.8806 | 1600 | 0.1971 | 0.9313 | 0.9300 | 0.9359 | 0.9313 | |
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| 0.1396 | 10.4961 | 1700 | 0.1527 | 0.9489 | 0.9484 | 0.9505 | 0.9489 | |
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### Framework versions |
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- Transformers 4.48.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.4.0 |
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- Tokenizers 0.21.0 |
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